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Quantitative Analysis for Management, Global Edition

Quantitative Analysis for Management, Global Edition

Barry Render | Ralph M. Stair | Michael E. Hanna | Trevor S. Hale

(2017)

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Book Details

Abstract

For courses in management science and decision modeling.

 

Foundational understanding of management science through real-world problems and solutions

Quantitative Analysis for Management helps students to develop a real-world understanding of business analytics, quantitative methods, and management science by emphasizing model building, tangible examples, and computer applications. The authors offer an accessible introduction to mathematical models and then students apply those models using step-by-step, how-to instructions. For more intricate mathematical procedures, the 13th Edition offers a flexible approach, allowing instructors to omit specific sections without interrupting the flow of the material. Supporting computer software enables instructors to focus on the managerial problems and solutions, rather than spending valuable class time on the details of algorithms.


Table of Contents

Section Title Page Action Price
Cover Cover
Title Page 1
Copyright Page 2
About the Authors 3
Brief Contents 5
Contents 6
Preface 13
Acknowledgments 17
Chapter 1: Introduction to Quantitative Analysis 19
1.1. What Is Quantitative Analysis? 20
1.2. Business Analytics 20
1.3. The Quantitative Analysis Approach 21
Defining the Problem 22
Developing a Model 22
Acquiring Input Data 22
Developing a Solution 23
Testing the Solution 23
Analyzing the Results and Sensitivity Analysis 24
Implementing the Results 24
The Quantitative Analysis Approach and Modeling in the Real World 24
1.4. How to Develop a Quantitative Analysis Model 24
The Advantages of Mathematical Modeling 27
Mathematical Models Categorized by Risk 27
1.5. The Role of Computers and Spreadsheet Models in the Quantitative Analysis Approach 27
1.6. Possible Problems in the Quantitative Analysis Approach 30
Defining the Problem 30
Developing a Model 31
Acquiring Input Data 32
Developing a Solution 32
Testing the Solution 32
Analyzing the Results 33
1.7. Implementation—Not Just the Final Step 33
Lack of Commitment and Resistance to Change 34
Lack of Commitment by Quantitative Analysts 34
Summary 34
Glossary 34
Key Equations 35
Self-Test 35
Discussion Questions and Problems 36
Case Study: Food and Beverages at Southwestern University Football Games 37
Bibliography 38
Chapter 2: Probability Concepts and Applications 39
2.1. Fundamental Concepts 40
Two Basic Rules of Probability 40
Types of Probability 40
Mutually Exclusive and Collectively Exhaustive Events 41
Unions and Intersections of Events 43
Probability Rules for Unions, Intersections, and Conditional Probabilities 43
2.2. Revising Probabilities with Bayes’ Theorem 45
General Form of Bayes’ Theorem 46
2.3. Further Probability Revisions 47
2.4. Random Variables 48
2.5. Probability Distributions 50
Probability Distribution of a Discrete Random Variable 50
Expected Value of a Discrete Probability Distribution 50
Variance of a Discrete Probability Distribution 51
Probability Distribution of a Continuous Random Variable 52
2.6. The Binomial Distribution 53
Solving Problems with the Binomial Formula 54
Solving Problems with Binomial Tables 55
2.7. The Normal Distribution 56
Area Under the Normal Curve 58
Using the Standard Normal Table 58
Haynes Construction Company Example 59
The Empirical Rule 62
2.8. The F Distribution 62
2.9. The Exponential Distribution 64
Arnold’s Muffler Example 65
2.10. The Poisson Distribution 66
Summary 68
Glossary 68
Key Equations 69
Solved Problems 70
Self-Test 72
Discussion Questions and Problems 73
Case Study: WTVX 79
Bibliography 79
Appendix 2.1: Derivation of Bayes’ Theorem 79
Chapter 3: Decision Analysis 81
3.1. The Six Steps in Decision Making 81
3.2. Types of Decision-Making Environments 83
3.3. Decision Making Under Uncertainty 83
Optimistic 84
Pessimistic 84
Criterion of Realism (Hurwicz Criterion) 85
Equally Likely (Laplace) 85
Minimax Regret 85
3.4. Decision Making Under Risk 87
Expected Monetary Value 87
Expected Value of Perfect Information 88
Expected Opportunity Loss 89
Sensitivity Analysis 90
A Minimization Example 91
3.5. Using Software for Payoff Table Problems 93
QM for Windows 93
Excel QM 93
3.6. Decision Trees 95
Efficiency of Sample Information 100
Sensitivity Analysis 100
3.7. How Probability Values Are Estimated by Bayesian Analysis 101
Calculating Revised Probabilities 101
Potential Problem in Using Survey Results 103
3.8. Utility Theory 104
Measuring Utility and Constructing a Utility Curve 104
Utility as a Decision-Making Criterion 106
Summary 109
Glossary 109
Key Equations 110
Solved Problems 110
Self-Test 115
Discussion Questions and Problems 116
Case Study: Starting Right Corporation 125
Case Study: Toledo Leather Company 125
Case Study: Blake Electronics 126
Bibliography 128
Chapter 4: Regression Models 129
4.1. Scatter Diagrams 130
4.2. Simple Linear Regression 131
4.3. Measuring the Fit of the Regression Model 132
Coefficient of Determination 134
Correlation Coefficient 134
4.4. Assumptions of the Regression Model 135
Estimating the Variance 137
4.5. Testing the Model for Significance 137
Triple A Construction Example 139
The Analysis of Variance (ANOVA) Table 140
Triple A Construction ANOVA Example 140
4.6. Using Computer Software for Regression 140
Excel 2016 140
Excel QM 141
QM for Windows 143
4.7. Multiple Regression Analysis 144
Evaluating the Multiple Regression Model 145
Jenny Wilson Realty Example 146
4.8. Binary or Dummy Variables 147
4.9. Model Building 148
Stepwise Regression 149
Multicollinearity 149
4.10. Nonlinear Regression 149
4.11. Cautions and Pitfalls in Regression Analysis 152
Summary 153
Glossary 153
Key Equations 154
Solved Problems 155
Self-Test 157
Discussion Questions and Problems 157
Case Study: North–South Airline 162
Bibliography 163
Appendix 4.1: Formulas for Regression Calculations 163
Chapter 5: Forecasting 165
5.1. Types of Forecasting Models 165
Qualitative Models 165
Causal Models 166
Time-Series Models 167
5.2. Components of a Time-Series 167
5.3. Measures of Forecast Accuracy 169
5.4. Forecasting Models—Random Variations Only 172
Moving Averages 172
Weighted Moving Averages 172
Exponential Smoothing 174
Using Software for Forecasting Time Series 176
5.5. Forecasting Models—Trend and Random Variations 178
Exponential Smoothing with Trend 178
Trend Projections 181
5.6. Adjusting for Seasonal Variations 182
Seasonal Indices 183
Calculating Seasonal Indices with No Trend 183
Calculating Seasonal Indices with Trend 184
5.7. Forecasting Models—Trend, Seasonal, and Random Variations 185
The Decomposition Method 185
Software for Decomposition 188
Using Regression with Trend and Seasonal Components 188
5.8. Monitoring and Controlling Forecasts 190
Adaptive Smoothing 192
Summary 192
Glossary 192
Key Equations 193
Solved Problems 194
Self-Test 195
Discussion Questions and Problems 196
Case Study: Forecasting Attendance at SWU Football Games 200
Case Study: Forecasting Monthly Sales 201
Bibliography 202
Chapter 6: Inventory Control Models 203
6.1. Importance of Inventory Control 204
Decoupling Function 204
Storing Resources 205
Irregular Supply and Demand 205
Quantity Discounts 205
Avoiding Stockouts and Shortages 205
6.2. Inventory Decisions 205
6.3. Economic Order Quantity: Determining How Much to Order 207
Inventory Costs in the EOQ Situation 207
Finding the EOQ 209
Sumco Pump Company Example 210
Purchase Cost of Inventory Items 211
Sensitivity Analysis with the EOQ Model 212
6.4. Reorder Point: Determining When to Order 212
6.5. EOQ Without the Instantaneous Receipt Assumption 214
Annual Carrying Cost for Production Run Model 214
Annual Setup Cost or Annual Ordering Cost 215
Determining the Optimal Production Quantity 215
Brown Manufacturing Example 216
6.6. Quantity Discount Models 218
Brass Department Store Example 220
6.7. Use of Safety Stock 221
6.8. Single-Period Inventory Models 227
Marginal Analysis with Discrete Distributions 228
Café du Donut Example 228
Marginal Analysis with the Normal Distribution 230
Newspaper Example 230
6.9. ABC Analysis 232
6.10. Dependent Demand: The Case for Material Requirements Planning 232
Material Structure Tree 233
Gross and Net Material Requirements Plans 234
Two or More End Products 236
6.11. Just-In-Time Inventory Control 237
6.12. Enterprise Resource Planning 238
Summary 239
Glossary 239
Key Equations 240
Solved Problems 241
Self-Test 243
Discussion Questions and Problems 244
Case Study: Martin-Pullin Bicycle Corporation 252
Bibliography 253
Appendix 6.1: Inventory Control with QM for Windows 253
Chapter 7: Linear Programming Models: Graphical and Computer Methods 255
7.1. Requirements of a Linear Programming Problem 256
7.2. Formulating LP Problems 257
Flair Furniture Company 258
7.3. Graphical Solution to an LP Problem 259
Graphical Representation of Constraints 259
Isoprofit Line Solution Method 263
Corner Point Solution Method 266
Slack and Surplus 268
7.4. Solving Flair Furniture’s LP Problem Using QM for Windows, Excel 2016, and Excel QM 269
Using QM for Windows 269
Using Excel’s Solver Command to Solve LP Problems 270
Using Excel QM 273
7.5. Solving Minimization Problems 275
Holiday Meal Turkey Ranch 275
7.6. Four Special Cases in LP 279
No Feasible Solution 279
Unboundedness 279
Redundancy 280
Alternate Optimal Solutions 281
7.7. Sensitivity Analysis 282
High Note Sound Company 283
Changes in the Objective Function Coefficient 284
QM for Windows and Changes in Objective Function Coefficients 284
Excel Solver and Changes in Objective Function Coefficients 285
Changes in the Technological Coefficients 286
Changes in the Resources or Right-Hand-Side Values 287
QM for Windows and Changes in Right-Hand- Side Values 288
Excel Solver and Changes in Right-Hand-Side Values 288
Summary 290
Glossary 290
Solved Problems 291
Self-Test 295
Discussion Questions and Problems 296
Case Study: Mexicana Wire Winding, Inc. 304
Bibliography 306
Chapter 8: Linear Programming Applications 307
8.1. Marketing Applications 307
Media Selection 307
Marketing Research 309
8.2. Manufacturing Applications 311
Production Mix 311
Production Scheduling 313
8.3. Employee Scheduling Applications 317
Labor Planning 317
8.4. Financial Applications 318
Portfolio Selection 318
Truck Loading Problem 321
8.5. Ingredient Blending Applications 323
Diet Problems 323
Ingredient Mix and Blending Problems 324
8.6. Other Linear Programming Applications 326
Summary 328
Self-Test 328
Problems 329
Case Study: Cable & Moore 336
Bibliography 336
Chapter 9: Transportation, Assignment, and Network Models 337
9.1. The Transportation Problem 338
Linear Program for the Transportation Example 338
Solving Transportation Problems Using Computer Software 339
A General LP Model for Transportation Problems 340
Facility Location Analysis 341
9.2. The Assignment Problem 343
Linear Program for Assignment Example 343
9.3. The Transshipment Problem 345
Linear Program for Transshipment Example 345
9.4. Maximal-Flow Problem 348
Example 348
9.5. Shortest-Route Problem 350
9.6. Minimal-Spanning Tree Problem 352
Summary 355
Glossary 356
Solved Problems 356
Self-Test 358
Discussion Questions and Problems 359
Case Study: Andrew–Carter, Inc. 370
Case Study: Northeastern Airlines 371
Case Study: Southwestern University Traffic Problems 372
Bibliography 373
Appendix 9.1: Using QM for Windows 373
Chapter 10: Integer Programming, Goal Programming, and Nonlinear Programming 375
10.1. Integer Programming 376
Harrison Electric Company Example of Integer Programming 376
Using Software to Solve the Harrison Integer Programming Problem 378
Mixed-Integer Programming Problem Example 378
10.2. Modeling with 0–1 (Binary) Variables 381
Capital Budgeting Example 382
Limiting the Number of Alternatives Selected 383
Dependent Selections 383
Fixed-Charge Problem Example 384
Financial Investment Example 385
10.3. Goal Programming 386
Example of Goal Programming: Harrison Electric Company Revisited 387
Extension to Equally Important Multiple Goals 388
Ranking Goals with Priority Levels 389
Goal Programming with Weighted Goals 389
10.4. Nonlinear Programming 390
Nonlinear Objective Function and Linear Constraints 391
Both Nonlinear Objective Function and Nonlinear Constraints 391
Linear Objective Function with Nonlinear Constraints 392
Summary 393
Glossary 393
Solved Problems 394
Self-Test 396
Discussion Questions and Problems 397
Case Study: Schank Marketing Research 402
Case Study: Oakton River Bridge 403
Bibliography 403
Chapter 11: Project Management 405
11.1. PERT/CPM 407
General Foundry Example of PERT/CPM 407
Drawing the PERT/CPM Network 408
Activity Times 409
How to Find the Critical Path 410
Probability of Project Completion 413
What PERT Was Able to Provide 416
Using Excel QM for the General Foundry Example 416
Sensitivity Analysis and Project Management 417
11.2. PERT/Cost 418
Planning and Scheduling Project Costs: Budgeting Process 418
Monitoring and Controlling Project Costs 421
11.3. Project Crashing 423
General Foundry Example 424
Project Crashing with Linear Programming 425
11.4. Other Topics in Project Management 428
Subprojects 428
Milestones 428
Resource Leveling 428
Software 428
Summary 428
Glossary 428
Key Equations 429
Solved Problems 430
Self-Test 432
Discussion Questions and Problems 433
Case Study: Southwestern University Stadium Construction 440
Case Study: Family Planning Research Center of Nigeria 441
Bibliography 442
Appendix 11.1: Project Management with QM for Windows 442
Chapter 12: Waiting Lines and Queuing Theory Models 445
12.1. Waiting Line Costs 446
Three Rivers Shipping Company Example 446
12.2. Characteristics of a Queuing System 447
Arrival Characteristics 447
Waiting Line Characteristics 448
Service Facility Characteristics 448
Identifying Models Using Kendall Notation 449
12.3. Single-Channel Queuing Model with Poisson Arrivals and Exponential Service Times (M/M/1) 452
Assumptions of the Model 452
Queuing Equations 452
Arnold’s Muffler Shop Case 453
Enhancing the Queuing Environment 456
12.4. Multichannel Queuing Model with Poisson Arrivals and Exponential Service Times (M/M/m) 457
Equations for the Multichannel Queuing Model 457
Arnold’s Muffler Shop Revisited 458
12.5. Constant Service Time Model (M/D/1) 460
Equations for the Constant Service Time Model 460
Garcia-Golding Recycling, Inc. 461
12.6. Finite Population Model (M/M/1 with Finite Source) 461
Equations for the Finite Population Model 462
Department of Commerce Example 462
12.7. Some General Operating Characteristic Relationships 463
12.8. More Complex Queuing Models and the Use of Simulation 464
Summary 464
Glossary 465
Key Equations 465
Solved Problems 467
Self-Test 469
Discussion Questions and Problems 470
Case Study: New England Foundry 475
Case Study: Winter Park Hotel 477
Bibliography 477
Appendix 12.1: Using QM for Windows 478
Chapter 13: Simulation Modeling 479
13.1. Advantages and Disadvantages of Simulation 480
13.2. Monte Carlo Simulation 481
Harry’s Auto Tire Example 482
Using QM for Windows for Simulation 486
Simulation with Excel Spreadsheets 487
13.3. Simulation and Inventory Analysis 489
Simkin’s Hardware Store 490
Analyzing Simkin’s Inventory Costs 493
13.4. Simulation of a Queuing Problem 494
Port of New Orleans 494
Using Excel to Simulate the Port of New Orleans Queuing Problem 496
13.5. Simulation Model for a Maintenance Policy 497
Three Hills Power Company 497
Cost Analysis of the Simulation 499
13.6. Other Simulation Issues 502
Two Other Types of Simulation Models 502
Verification and Validation 503
Role of Computers in Simulation 503
Summary 504
Glossary 504
Solved Problems 505
Self-Test 507
Discussion Questions and Problems 508
Case Study: Alabama Airlines 514
Case Study: Statewide Development Corporation 515
Case Study: FB Badpoore Aerospace 516
Bibliography 518
Chapter 14: Markov Analysis 519
14.1. States and State Probabilities 520
The Vector of State Probabilities for Grocery Store Example 521
14.2. Matrix of Transition Probabilities 522
Transition Probabilities for Grocery Store Example 522
14.3. Predicting Future Market Shares 523
14.4. Markov Analysis of Machine Operations 524
14.5. Equilibrium Conditions 525
14.6. Absorbing States and the Fundamental Matrix: Accounts Receivable Application 528
Summary 532
Glossary 532
Key Equations 532
Solved Problems 533
Self-Test 536
Discussion Questions and Problems 537
Case Study: Rentall Trucks 541
Bibliography 543
Appendix 14.1: Markov Analysis with QM for Windows 543
Appendix 14.2: Markov Analysis with Excel 544
Chapter 15: Statistical Quality Control 547
15.1. Defining Quality and TQM 547
15.2. Statistical Process Control 549
Variability in the Process 549
15.3. Control Charts for Variables 550
The Central Limit Theorem 551
Setting x-Chart Limits 552
Setting Range Chart Limits 554
15.4. Control Charts for Attributes 555
p-Charts 555
c-Charts 557
Summary 559
Glossary 559
Key Equations 559
Solved Problems 560
Self-Test 561
Discussion Questions and Problems 561
Bibliography 564
Appendix 15.1: Using QM for Windows for SPC 565
Appendices 567
Appendix A: Areas Under the Standard Normal Curve 568
Appendix B: Binomial Probabilities 570
Appendix C: Values of e for Use in the Poisson Distribution 575
Appendix D: F Distribution Values 576
Appendix E: Using POM-QM for Windows 578
Appendix F: Using Excel QM and Excel Add-Ins 581
Appendix G: Solutions to Selected Problems 582
Appendix H: Solutions to Self-Tests 586
Index 589
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